Weekly Exercises #4

library(tidyverse)     # for data cleaning and plotting
library(lubridate)     # for date manipulation
library(openintro)     # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data
library(ggmap)         # for mapping points on maps
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(ggthemes)      # for more themes (including theme_map())
theme_set(theme_minimal())

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

Warm-up exercises from tutorial

These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?

world <- get_stamenmap(
    bbox = c(left = -170, bottom = -57, right = 186, top = 76), 
    maptype = "terrain",
    zoom = 2)

ggmap(world) + 
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude, 
                 color = `Ownership Type`), alpha = 0.5, size = .1) +
  labs(title = "Starbucks location and ownership type world wide") +
  scale_color_viridis_d() + 
  theme_map() + 
  theme(plot.title = element_text(face = "bold", hjust = 0.5)) + 
  guides(colour = guide_legend(override.aes = list(size = 2, alpha = 1))) + 
  theme(legend.background = element_blank(),
    legend.box.background = element_blank(),
    legend.key = element_blank())

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2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

starbucks_mn <- Starbucks %>% 
  filter(Country == "US", `State/Province` == "MN")

minneapolis <- get_stamenmap(
    bbox = c(left = -93.44, bottom = 44.77, right = -92.76, top = 45.10), 
    maptype = "terrain",
    zoom = 11)

ggmap(minneapolis) + 
  geom_point(data = starbucks_mn, 
             aes(x = Longitude, y = Latitude),
             alpha = 1, 
             size = 1.5,
             color = "forestgreen") +
  labs(title = "Starbucks location in the Twin Cities, MN, USA") +
  theme_map() + 
  theme(plot.title = element_text(face = "bold", hjust = 0.5))

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3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map).

minne_diff_zoom <- get_stamenmap(
    bbox = c(left = -93.44, bottom = 44.77, right = -92.76, top = 45.10), 
    maptype = "terrain",
    zoom = 9)

ggmap(minne_diff_zoom)  +
  labs(title = "Map of Twin Cities, MN, USA") +
  theme_map() + 
  theme(plot.title = element_text(face = "bold", hjust = 0.5))

• The zoom number is the level of details that you want your map to show. Smaller numbers show less detail and larger numbers show more.

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4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

starbucks_mn <- Starbucks %>% 
  filter(Country == "US", `State/Province` == "MN")

watercolor_mn <- get_stamenmap(
    bbox = c(left = -93.44, bottom = 44.77, right = -92.76, top = 45.10), 
    maptype = "watercolor",
    zoom = 11)

ggmap(watercolor_mn) + 
  geom_point(data = starbucks_mn, 
             aes(x = Longitude, y = Latitude),
             alpha = 1, 
             size = 1.5,
             color = "black") +
  labs(title = "Starbucks location in the Twin Cities, MN, USA") +
  theme_map() + 
  theme(plot.title = element_text(face = "bold", hjust = 0.5))

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5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do this, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

mac_and_starbucks <- get_stamenmap(
    bbox = c(left = -93.7206, bottom = 44.6813, 
             right = -92.7202, top = 45.2837), 
    maptype = "terrain",
    zoom = 10)

ggmap(mac_and_starbucks) + 
  geom_point(data = starbucks_mn, 
             aes(x = Longitude, y = Latitude),
             alpha = 1, 
             size = 1,
             color = "forestgreen") +
  annotate("point", x = -93.17123, y = 44.93790, color = "red") +
  annotate("text", x = -93.17123, y = 44.93, 
           color = "red", label = "Macalester College") + 
  labs(title = "Starbucks locations in Twin Cities, MN",
       subtitle = "In relation to Macalester College") +
  theme_map() + 
  theme(plot.title = element_text(face = "bold", hjust = 0.5),
        plot.subtitle = element_text(face = "bold", hjust = 0.5))

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Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

starbucks_with_2018_pop_est <-
  starbucks_us_by_state %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% 
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)

6. dplyr review: Look through the code above and describe what each line of code does.

• census_pop_est_2018 reads the data census population estimate in 2018
• separate() separates the dot in front of state names and creates a separate column for the dot and the state names
• select() recreates the table but takes out the dot column
• mutate() transforms all state names into lower case
• starbucks_with_2018_pop_est is the new variable created by joining starbucks_us_by_state and census_pop_est_2018
• left_join() joins census_pop_est_2018 data with starbucks_us_by_state by the variables state and state_name, respectively.
• mutate() creates new variable, starbucks_per_10000, by calculating how many Starbucks there are per 10000 people

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7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.

states_map <- map_data("state")

starbucks_with_2018_pop_est %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state_name,
               fill = starbucks_per_10000)) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  geom_point(data = Starbucks %>% filter(`Country` == "US", 
                                         `State/Province` != "AK", 
                                         `State/Province` != "HI"),
             aes(x = Longitude, y = Latitude),
             size = .05,
             alpha = .2, 
             color = "gold") +
  scale_fill_gradient2(low = "lightskyblue1", mid = "paleturquoise2", high = "forestgreen") + 
  labs(title = "Starbucks location per 10,000 people in the US",
       x = "Longitude",
       y = "Latitude",
       caption = "Jennifer Huang",
       fill = "Starbucks per 10,000") + 
  theme_map() + 
  theme(legend.background = element_blank(),
        legend.position = "left",
        plot.title = element_text(face = "bold", hjust = 0.5))

• Starbucks mainly exist in the West and East coasts. It’s apparently really not a big thing in the Mid-West and somewhat in the South. But interestingly, in Colorado where it seems to be Denver, there’s a cluster of Starbucks locations. The coffe franchise goes where big ciites are, New York City, Denver, San Francisco, LA, Boston, Philadelphia, etc.

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A few of your favorite things (leaflet)

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not.

fav_taiwan_jen <- tibble(
  place = c("ShiFen Old Street", "HouTong Cat Village", "JiouFen Mountain Town",
            "TianMu District", "TamSui District", "Home", 
            "LungShan Temple", "HuaShan 1914 Creative Park",
            "ZhongXiao East Road", "Taipei 101"),
  long = c(121.7767, 121.8275, 121.8463,
           121.5341, 121.5150, 121.4434,
           121.4999, 121.5293, 
           121.5429, 121.5645),
  lat = c(25.0427, 25.0870, 25.1092,
          25.1157, 25.1152, 25.1720,
          25.0372, 25.0441, 
          25.0411, 25.0340),
  TopThree = place %in% c("JiouFen Mountain Town", "TamSui District", "ZhongXiao East Road")
  )

• Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

pal_col <- colorFactor("viridis", domain = fav_taiwan_jen$TopThree)

leaflet(data = fav_taiwan_jen) %>% 
  addTiles() %>% 
  addCircles(lng = ~long,
             lat = ~lat,
             opacity = 1,
             radius = 50,
             color = ~pal_col(TopThree)) %>% 
  addPolylines(lng = ~long, 
               lat = ~lat, 
               color = col2hex("darkblue")) %>% 
  addLegend(position = c("topright"),
            pal = pal_col,
            values = ~TopThree,
            opacity = 1) %>% 
  addMarkers(
    lng = 121.7767, lat = 25.0427,
    label = "ShiFen Old Street",
    labelOptions = labelOptions(noHide = T)) %>% 
  addMarkers(
    lng = 121.8275, lat = 25.0870,
    label = "HouTong Cat Village",
    labelOptions = labelOptions(noHide = T)) %>% 
  addMarkers(
    lng = 121.8463, lat = 25.1092,
    label = "JiouFen Mountain Town",
    labelOptions = labelOptions(noHide = T,
      style = list("color" = "goldenrod", "font-style" = "italic"))) %>% 
  addMarkers(
    lng = 121.5341, lat = 25.1157,
    label = "TianMu District",
    labelOptions = labelOptions(noHide = T,
      style = list("color" = "goldenrod", "font-style" = "italic"))) %>% 
  addMarkers(
    lng = 121.5150, lat = 25.1152,
    label = "TamSui District",
    labelOptions = labelOptions(noHide = T, direction = "bottom")) %>% 
  addMarkers(
    lng = 121.4434, lat = 25.1720,
    label = "Home",
    labelOptions = labelOptions(noHide = T)) %>% 
  addMarkers(
    lng = 121.4999, lat = 25.0372,
    label = "LungShan Temple",
    labelOptions = labelOptions(noHide = T, direction = "left")) %>% 
  addMarkers(
    lng = 121.5293, lat = 25.0441,
    label = "HuaShan Creative Park",
    labelOptions = labelOptions(noHide = T, direction = "bottom")) %>% 
  addMarkers(
    lng = 121.5429, lat = 25.0411,
    label = "ZhongXiao E.Road",
    labelOptions = labelOptions(noHide = T, direction = "top",
      style = list("color" = "goldenrod", "font-style" = "italic"))) %>% 
  addMarkers(
    lng = 121.5645, lat = 25.0340,
    label = "Taipei 101",
    labelOptions = labelOptions(noHide = T, direction = "bottom"))

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Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

DC_map <- get_stamenmap(
  bbox = c(left = -77.2035, bottom = 38.7840, 
           right = -76.8498, top = 39.0126),
  maptype = "terrain",
  zoom = 12)

station_trips <- Trips %>% 
  mutate(name = sstation) %>% 
  left_join(Stations,
            by = c("name")) %>% 
  group_by(lat, long) %>% 
  summarize(freq_stations = n())

ggmap(DC_map) + 
  geom_point(data = station_trips,
             aes(x = long, y = lat, color = freq_stations),
             alpha = 1,
             size = 1) + 
  scale_color_viridis_c() +
  labs(title = "Total number of departures from each bike station in DC",
       col = "Frequency") +
  theme_map() + 
  theme(legend.background = element_blank(),
        legend.position = "left",
        plot.title = element_text(face = "bold", hjust = 0.5))

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10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are.

departures <- Trips %>% 
  left_join(Stations, by = c("estation" = "name")) %>% 
  group_by(lat, long) %>% 
  summarize(n = n(), probability = mean(client == "Casual"))

ggmap(DC_map) +
  geom_point(data = departures,
             aes(x = long, y = lat, color = probability),
             alpha = 1,
             size = 1) +
  scale_color_viridis_c() +
  labs(title = "Areas in DC with high percentage of departures by casual bike users",
       col = "Percentage of casual bikers") + 
  theme_map() + 
  theme(legend.background = element_blank(),
        legend.position = "left",
        plot.title = element_text(face = "bold", hjust = 0.5))

• It seems like most casual bikers cluster in center of the city perhaps because that’s where most of the sightseeing spots are in DC.

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COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map?

states_map <- map_data("state")

covid19 %>% 
  group_by(state) %>% 
  summarize(most_recent_case = max(cases)) %>%
  mutate(state = str_to_lower(state)) %>% 
  # since its cumulative, we can use max(), if not we can't
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state, fill = most_recent_case)) +
  scale_fill_viridis_c() +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  labs(title = "Most recent cumulative number of COVID cases in US",
       fill = "Number of cases") +
  theme_map() +
  theme(legend.background = element_blank(),
        legend.position = "left",
        plot.title = element_text(face = "bold", hjust = 0.5))

• This graph is problematic because it only shows the most recent count of COVID cases but neglects population in each state. Including population data in reporting COVID cases is necessary so that people know the proportion of sick people to healthy people.
• This map is misleading because it shows that California has the least case count, which is not true. California continues to have the worst COVID hit, even after a year of battling with COVID.

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12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

states_map <- map_data("state")

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

covid_with_2018_pop_est <-
  covid19 %>% 
  mutate(state = str_to_lower(state)) %>% 
  left_join(census_pop_est_2018,
            by = c("state" = "state")) %>% 
  mutate(most_recent_case = max(cases),
         covid_per_10000 = (most_recent_case/est_pop_2018)*10000) %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state, fill = covid_per_10000)) +
  scale_fill_viridis_c() +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  labs(title = "Most recent cumulative number of COVID cases per 10,000 people in US",
       fill = "Cases per 10,000 people") +
  theme_map() +
  theme(legend.background = element_blank(),
        legend.position = "left",
        plot.title = element_text(face = "bold", hjust = 0.5))

covid_with_2018_pop_est

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13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?

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Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

mpls_suspicious <- MplsStops %>% 
  mutate(problem_numeric = problem %in% c("suspicious")) %>% 
  group_by(neighborhood) %>% 
  summarize(number_of_stops = n(),
            prop_sus = mean(problem_numeric))

mpls_suspicious

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircle, set stroke = FAlSE, use colorFactor() to create a palette.

pal_police <- colorFactor("viridis", domain = MplsStops$problem)

MplsStops %>% 
  leaflet() %>% 
  addTiles() %>% 
  addCircles(lng = ~long,
             lat = ~lat,
             opacity = 0.4,
             radius = 2,
             color = ~pal_police(problem)) %>% #apply color created to problem variable
  addLegend(position = c("topright"),
            pal = pal_police,
            values = ~problem,
            opacity = 2) 

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16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE) %>%
  rename(neighborhood = "BDNAME")

mpls_all <- mpls_nbhd %>% 
  left_join(MplsDemo, by = c("neighborhood")) %>% 
  left_join(mpls_suspicious, by = c("neighborhood"))

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17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE) %>%
  rename(neighborhood = "BDNAME")

mpls_all <- mpls_nbhd %>% 
  left_join(MplsDemo, by = c("neighborhood")) %>% 
  left_join(mpls_suspicious, by = c("neighborhood"))

pal_mpls <- colorNumeric("viridis", domain = mpls_all$prop_sus)

leaflet(mpls_all) %>% 
  addTiles() %>% 
  addPolygons(
    stroke = FALSE,
    fillColor = ~pal_mpls(prop_sus),
    fillOpacity = 0.8,
    smoothFactor = 0.7, 
    highlight = highlightOptions(weight = 9, 
                                 color = "black",
                                 fillOpacity = 10,
                                 bringToFront = FALSE)) %>%
  addLegend(pal = pal_mpls, 
            values = ~prop_sus, 
            opacity = 0.8, 
            title = NULL,
            position = "bottomright")

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18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.

• I am interested in finding out the distribution of licensed and company owned Starbucks in California. Licensed Starbucks usually appear in Target or inside some larger buildings while company owned Starbucks appears to be an individual store.
• This map shows the overall cluster of Starbucks locations: San Francisco and Los Angeles. Within those two big cities in California, it seems like they each have roughly the same amount of licensed and company owned Starbucks.
• It is also worth noting that San Diego has the third most Starbucks locations in California, at least according to this map.

starbucks_CA <- Starbucks %>% 
  filter(Country == "US", `State/Province` == "CA")

pal_starbucks <- colorFactor("viridis", domain = starbucks_CA$`Ownership Type`)

leaflet(starbucks_CA) %>% 
  addTiles() %>% 
  addCircles(lng = ~Longitude,
             lat = ~Latitude,
             opacity = 0.5,
             radius = 50,
             color = ~pal_starbucks(`Ownership Type`)) %>% 
  addLegend(position = c("topright"),
            pal = pal_starbucks,
            values = ~`Ownership Type`,
            opacity = 2) 

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GitHub link

Link to Jennifer’s Weekly Exercise 4 GitHub Page